When Lending Decisions Go Social

As the story goes, a man purchased a donkey, but before he finalized the sale, he asked the farmer if he could take him home and try him out. When he did, and he observed that the first thing the donkey did was seek out the laziest donkeys to hang out with, he immediately returned him. His explanation was that he didn’t need to see how he performed at work, all he needed to see was which animals he wanted to associate with and that told him everything he needed to know.

The moral of the story is simple: “You are known by the company you keep.”

And now, that company, and the social activities that people are doing as part of their social networks, is the basis for making lending decisions, especially in developing companies where that may be the only reliable “thick file” of data on a person that exists.

The Financial Times recently reported on Jubao and Tencent, two Chinese online finance firms, using social media data to assess credit risk, more or less, by the company they keep. According to Jesse Chen, co-founder of Jubao Internet Technology, a top P2P lending agency in China, “If we find you are [social media] friends with celebrities from the entertainment or finance industries, we think you must be to some extent trustworthy . . . since otherwise you wouldn’t have such friends.”

There are two fundamental reasons why China is using data collected from social media to fuel its lending industry, Madhu explained. First, social media use is rampant in China, as it is in the U.S., and established social media platforms are now a legacy providing rich resources for firms seeking valuable personal data. Second, China’s centralized credit scoring system is largely incomplete.

Madhu said that in China, traditional credit rating data such as banks’ records of loan defaults and repayments, are held by the Central Bank, but these data only cover one-third of the population and, up to a year ago, were only accessible to banks. Even though the recent decision to expand access to eight companies, including Tencent and Alibaba, opens up access, the data that can be obtained from those archives doesn’t cover the vast majority of people for whom borrowing money is attractive.

That, Madhu explains, is why Tencent’s WeChat is such an attractive and useful repository for the kind of data that would help the country’s alternative lenders better know their potential borrowers. Its 762 million people do just about everything within the WeChat ecosystem: chat with a friend about where to go to eat, place an order on that restaurant’s website, turn up at the restaurant, see the order on the restaurant’s app order screen and pay for the order — all without ever exiting the WeChat platform.

Madhu is confident that using social media to assess lending risk will grow very quickly in developing countries with limited credit system infrastructure and where the unbanked and underbanked have little means of obtaining financing. He also sees this type of microfinancing model increasing in developed countries such as the U.S., albeit it slowly, where credit models expand to include more transaction-based decisioning for consumers who don’t necessary want – or have access to — a credit card or a credit line, but just want credit extended for a specific purchase at a moment in time.

And as that happens, Madhu believes that lenders are bound to increase their reliance on the intersection of social media and big data models. By 2028, 80 percent of the workforce will be millennials and, according to Madhu, “Millennials are not buying houses; they are not buying cars. They are using Uber, and the lack of portability with the current credit system creates a lot of friction for millennials.”

Those millennials may not buy cars or houses, but they sure leave a lot of social exhaust. And the company that they keep may be the key to unlocking many new opportunities for lenders to assess their creditworthiness and give them credit, where credit is due.